Summary of Human-in-the-loop Feature Selection Using Interpretable Kolmogorov-arnold Network-based Double Deep Q-network, by Md Abrar Jahin et al.
Human-in-the-Loop Feature Selection Using Interpretable Kolmogorov-Arnold Network-based Double Deep Q-Network
by Md Abrar Jahin, M. F. Mridha, Nilanjan Dey
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes an innovative approach to machine learning model interpretability and adaptability in high-dimensional spaces. The authors develop a Human-in-the-Loop (HITL) feature selection framework integrated into a Double Deep Q-Network (DDQN) using a Kolmogorov-Arnold Network (KAN). This novel method leverages simulated human feedback and stochastic distribution-based sampling to iteratively refine feature subsets per data instance, improving flexibility in feature selection. The KAN-DDQN model achieves notable test accuracies on MNIST and FashionMNIST datasets, outperforming conventional MLP-DDQN models. The proposed framework provides high interpretability via symbolic representation while using fewer neurons than MLPs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new way to make machine learning models better and easier to understand. It uses a special type of network called a Double Deep Q-Network (DDQN) and combines it with something called Human-in-the-Loop (HITL) feature selection. This means that the model can adjust its features based on feedback from humans. The result is a more accurate and understandable model that uses fewer neurons than other models. |
Keywords
» Artificial intelligence » Feature selection » Machine learning
